Related Google Professional-Data-Engineer Exams - Exam Professional-Data-Engineer Vce Format

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Google Certified Professional Data Engineer Exam Sample Questions (Q333-Q338):

NEW QUESTION # 333
You migrated your on-premises Apache Hadoop Distributed File System (HDFS) data lake to Cloud Storage. The data scientist team needs to process the data by using Apache Spark and SQL. Security policies need to be enforced at the column level. You need a cost-effective solution that can scale into a data mesh. What should you do?

A. 1. Define a BigLake table.
2. Create a taxonomy of policy tags in Data Catalog.
3. Add policy lags to columns.
4. Process with the Spark-BigQuery connector or BigQuery SOL.
B. 1. Deploy a long-living Dalaproc cluster with Apache Hive and Ranger enabled.
2. Configure Ranger for column level security.
3. Process with Dataproc Spark or Hive SQL.
C. 1 Apply an Identity and Access Management (IAM) policy at the file level in Cloud Storage
2. Define a BigQuery external table for SQL processing.
3. Use Dataproc Spark to process the Cloud Storage files.
D. 1. Load the data to BigQuery tables.
2. Create a taxonomy of policy tags in Data Catalog.
3. Add policy tags to columns.
4. Procoss with the Spark-BigQuery connector or BigQuery SQL.

Answer: C

Explanation:
For automating the CI/CD pipeline of DAGs running in Cloud Composer, the following approach ensures that DAGs are tested and deployed in a streamlined and efficient manner.
Use Cloud Build for Development Instance Testing:
Use Cloud Build to automate the process of copying the DAG code to the Cloud Storage bucket of the development instance.
This triggers Cloud Composer to automatically pick up and test the new DAGs in the development environment.
Testing and Validation:
Ensure that the DAGs run successfully in the development environment.
Validate the functionality and correctness of the DAGs before promoting them to production.
Deploy to Production:
If the DAGs pass all tests in the development environment, use Cloud Build to copy the tested DAG code to the Cloud Storage bucket of the production instance.
This ensures that only validated and tested DAGs are deployed to production, maintaining the stability and reliability of the production environment.
Simplicity and Reliability:
This approach leverages Cloud Build's capabilities for automation and integrates seamlessly with Cloud Composer's reliance on Cloud Storage for DAG storage.
By using Cloud Storage for both development and production deployments, the process remains simple and robust.
Google Data Engineer Reference:
Cloud Composer Documentation
Using Cloud Build
Deploying DAGs to Cloud Composer
Automating DAG Deployment with Cloud Build
By implementing this CI/CD pipeline, you ensure that DAGs are thoroughly tested in the development environment before being automatically deployed to the production environment, maintaining high quality and reliability.
Topic 3, MJTelco Case Study
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production - to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
Provide reliable and timely access to data for analysis from distributed research workers Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
Ensure secure and efficient transport and storage of telemetry data
Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records/day Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.

NEW QUESTION # 334
You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a machine-learning process. You want to support a logistic regression model. You also need to monitor and adjust for null values, which must remain real-valued and cannot be removed. What should you do?

A. Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataproc job.
B. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to 0 using a custom script.
C. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataprep job.
D. Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 0 using a Cloud Dataprep job.

Answer: C

NEW QUESTION # 335
Your organization is modernizing their IT services and migrating to Google Cloud. You need to organize the data that will be stored in Cloud Storage and BigQuery. You need to enable a data mesh approach to share the data between sales, product design, and marketing departments What should you do?

A. 1Create a project for storage of the data for your organization.
2 Create a central Cloud Storage bucket with three folders to store the files for each department.
3. Create a central BigQuery dataset with tables prefixed with the department name.
4 Give viewer rights for the storage project for the users of your departments.
B. 1Create a project for storage of the data for each of your departments.
2 Enable each department to create Cloud Storage buckets and BigQuery datasets.
3. Create user groups for authorized readers for each bucket and dataset.
4 Enable the IT team to administer the user groups to add or remove users as the departments' request.
C. 1 Create multiple projects for storage of the data for each of your departments' applications.
2 Enable each department to create Cloud Storage buckets and BigQuery datasets.
3. Publish the data that each department shared in Analytics Hub.
4 Enable all departments to discover and subscribe to the data they need in Analytics Hub.
D. 1 Create multiple projects for storage of the data for each of your departments' applications.
2 Enable each department to create Cloud Storage buckets and BigQuery datasets.
3 In Dataplex, map each department to a data lake and the Cloud Storage buckets, and map the BigQuery datasets to zones.
4 Enable each department to own and share the data of their data lakes.

Answer: C

Explanation:
Implementing a data mesh approach involves treating data as a product and enabling decentralized data ownership and architecture. The steps outlined in option C support this approach by creating separate projects for each department, which aligns with the principle of domain-oriented decentralized data ownership. By allowing departments to create their own Cloud Storage buckets and BigQuery datasets, it promotes autonomy and self-service. Publishing the data in Analytics Hub facilitates data sharing and discovery across departments, enabling a collaborative environment where data can be easily accessed and utilized by different parts of the organization.
References:
* Architecture and functions in a data mesh - Google Cloud
* Professional Data Engineer Certification Exam Guide | Learn - Google Cloud
* Build a Data Mesh with Dataplex | Google Cloud Skills Boost

NEW QUESTION # 336
Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the data. Which three machine learning applications can you use? (Choose three.)

A. Unsupervised learning to predict the location of a transaction.
B. Supervised learning to predict the location of a transaction.
C. Unsupervised learning to determine which transactions are most likely to be fraudulent.
D. Reinforcement learning to predict the location of a transaction.
E. Clustering to divide the transactions into N categories based on feature similarity.
F. Supervised learning to determine which transactions are most likely to be fraudulent.

Answer: B,C,E

Explanation:
Fraud is not a feature, so unsupervised, location is given so supervised, Clustering can be done looking at the done with same features.

NEW QUESTION # 337
You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

A. Cloud Scheduler
B. Workflow Templates on Cloud Dataproc
C. Cloud Composer
D. cron

Answer: B

NEW QUESTION # 338
......

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RELATED GOOGLE PROFESSIONAL-DATA-ENGINEER EXAMS - EXAM PROFESSIONAL-DATA-ENGINEER VCE FORMAT

Related Google Professional-Data-Engineer Exams - Exam Professional-Data-Engineer Vce Format